Noise is inherent to any communication system and, in television broadcasting networks, originates in different ways. At the source of image, noise may be either generated by photon or thermal noise in a video camera or by granular noise in films of cameras. Signal processing circuits and recorder play back systems add noise because the signal circulates in an intrinsically noisy loop.
Finally, the signal may be carried to the transmitter via a distribution network that adds more noise, and broadcast via an intrinsically noisy transmission medium. The receiver circuits themselves add noise. Many techniques have been developed to reduce the problem of Gaussian noise and these are described in literature. The adaptivity of noise level to a certain image/video sequence is a key feature of the so-called "smart" filters.
Generally, this adaptivity is obtained using non-linear techniques, such as averaging filters or filters with variable behavior depending on the estimation of some parameters. For instance, many known filters are obtained using scale and position estimates, or by estimating the temporal correlation (Recursive Averaging Filters) which is the most common techniques presently used in TV environments.
These known techniques have intrinsic disadvantages and/or limitations. In the case of a video sequence strongly affected by noise, the filter may confuse the noise with motion effects resulting in a poor noise reduction effect. To improve this situation, the user could automatically select from among a set of preordered response characteristics, such as to dynamically accentuate or reduce the filtering effect. However, these systems of arbitrary selection influence the temporal frequency, introducing a sort of "comet effect" which may become more annoying than an excessive edge smoothing. On the other hand, motion detection requires at least one field memory in the recursive loop of the filter with the consequent increment of total memory requirement for the system.